With the continuous development of artificial intelligence and cloud computing technologies, the industry is undergoing a transformation from centralised computing power to edge intelligence, and from traditional large-scale data centres to distributed ecosystems. This article explores three key trend forecasts and introduces the corresponding measures in hardware and system architecture, showcasing a technical roadmap that can be drawn upon in the construction of future ecosystems.
I. Industry Trend Forecasts
1. The New Game of Ecological Barriers and Hardware–Software Synergy
Trend Forecast:
At present, because core technologies are not fully open, mature ecosystems such as CUDA have established strong market barriers. However, this closed model may also stimulate developers to engage in reverse engineering and independent optimisation from the hardware level. Some projects have already begun to demonstrate success in challenging the constraints of the current ecosystem.
Industry Implications:
As the capabilities for hardware–software co-design improve, more open, efficient and diverse computing platforms may emerge in the future, driving the entire industry towards a more flexible and transparent direction.
2. From Centralised Data Centres to Distributed and Edge Intelligence
Trend Forecast:
In future, the primary applications of AI computing power will not be confined to the centralised computing of traditional data centres. Instead, they will gradually extend to high-demand tasks such as scientific research, technological breakthroughs, and smart data mining. Meanwhile, the growing prominence of data privacy and security concerns will inevitably drive the shift of computing power towards distributed and edge deployments, breaking the monopoly of existing giants over data and computing resources.
Industry Implications:
This transformation will enable terminal devices to possess stronger intelligent computing capabilities, achieving high-precision computation in offline or local environments, reducing data transmission risks, and giving rise to a more open and diverse technological ecosystem.
3. Focusing on the Simulation of Rational Thought to Achieve Complete Intelligence
Trend Forecast:
In the exploration of artificial general intelligence (AGI), the key does not lie in simply stacking computing power and data, but in constructing a computing environment capable of simulating human rational thought. Consciousness and subjective experience may be more of a phenomenon; in practical applications, the engineering focus should be on efficiently simulating rationality, logic, and decision-making.
Industry Implications:
Focusing on the engineering simulation of rational thought can help achieve levels of intelligence in specific areas that surpass human performance, while simultaneously avoiding the risks associated with subjective experience. This provides a clear pathway to further enhance the overall engineering efficiency of the system.
II. Technological Response Measures
1. Breakthrough in the Three-Dimensional Concurrent Computing Accelerator Chip
Technical Overview:
A new architecture computing chip has been successfully designed, validated, and entered mass production. This chip, through the deployment of larger memory and a larger-scale computing matrix at the hardware level, employs “three-dimensional concurrent computing acceleration” technology, achieving a significant improvement in multi-task parallel processing and data throughput.
Response Strategy:
The construction of a hardware–software co-design and an efficient internal server communication architecture provides robust support for the training and deployment of large-scale models. This architecture not only meets the current high demands for computing power and efficiency but also lays the foundation for the flexible expansion of future ecosystems.
2. Local Deployment of Large Models and Edge Intelligence Solutions
Technical Overview:
By utilising the three-dimensional concurrent computing chip, the ability to deploy high-precision large models on local terminals has been achieved, enabling devices to perform intelligent computations without the need for an Internet connection.
Response Strategy:
This approach reduces the privacy risks associated with data transmission, promotes the expansion of computing power from centralised data centres to edge intelligence, and provides strong support for data security, real-time responsiveness, and low-latency applications. At the same time, the development of related software ecosystems (such as operating systems, development toolchains, and model acceleration technologies) is being advanced in tandem.
3. Simulating Rational Thought to Build Engineering Practices for Complete Intelligence
Technical Overview:
In exploring the AGI direction, the focus is on efficiently simulating the rational aspects of human thought. By constructing a Turing-complete computing environment and achieving outstanding performance in specific fields, the goal is to gradually narrow the gap with general intelligence.
Response Strategy:
By avoiding the complexities of subjective experience and emotion in the engineering process, priority is given to breakthroughs in rationality, logic, and decision-making. This enables the system to surpass human performance in certain dimensions, accumulating the experience and data support necessary for building a fully intelligent system.
III. Looking to the Future
The current technological breakthroughs and validation of new architectures have brought a wealth of possibilities to the AI field. From hardware innovation to the realisation of edge intelligence, from data privacy protection to the simulation of rational thought, these trends and response measures paint a blueprint for a future AI ecosystem that is open, diverse, and highly efficient.
This process will not only drive the continuous evolution of technology but is also likely to break the existing monopoly on resources, promoting a more decentralised and secure collaboration of computing power and data on a global scale. Future intelligent systems will better serve a wide range of industries, driving society towards a new balance between innovation and efficiency.